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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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Optimizing 3D Clothing Models for VR: A Deep Learning Approach to Triangle Count Reduction

Optimizing 3D Clothing Models for VR: A Deep Learning Approa...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Surasachai Doungtap Varinya Phanichraksaphong Jenq-Haur Wang International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This study presents a deep learning framework optimizing 3D clothing models for VR, using a CNN to significantly reduce the triangle count of models from DeepFashion3D and CAP-UDF datasets. Achieving a balance between efficiency and detail, it cuts triangle count from over 160,000 to below 4,000, maintaining high DPI. The approach automates optimization, promising scalability and efficiency in VR fashion, setting a foundation for future 3D content development, enhancing virtual garment realism and interactivity.

关键词： Deep learning Solid modeling Three-dimensional displays Scalability Clothing Optimization Consumer electronics

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A Game-Theoretic Approach for Pricing and Determining Quality Levels of Cybersecurity Products Under an Exogenous Information-Sharing program 19th

A Game-Theoretic Approach for Pricing and Determining Qualit...

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19th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2022

作者： Rasti-Barzoki, Morteza Altmann, Jörn Egger, Bernhard Department of Industrial and Systems Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute of Engineering Research College of Engineering Seoul National University Seoul08826 Korea Republic of Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9783031293146

Price and quality level of products are two important decisions of any business. This paper provides equilibrium solutions for these decisions of two players for a cybersecurity ecosystem, including a solution provider and an information provider. We assume that end users join a cybersecurity ecosystem based on the prices and qualities of the solution provider’s and information provider’s products;so that, the increasing of the prices or/and decreasing the qualities will reduce the number of end-users of that ecosystem. Also, it is assumed that there is an exogenous information-sharing program under which the effect of quality of the information provider’s product on the cybersecurity level depends on the level of information-sharing. Under this information-sharing program, the equilibrium solutions of prices and qualities of these two players are given. Also, some results and insights are given with a numerical example. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Game theory

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A Game-Theoretic Approach for Pricing and Determining Virtual Reality Investment and Cybersecurity Level in a Dual-Channel Supply Chain 19th

A Game-Theoretic Approach for Pricing and Determining Virtua...

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19th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2022

作者： Alaei, Amir Mohammad Rasti-Barzoki, Morteza Altmann, Jörn Egger, Bernhard Department of Industrial and Systems Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute of Engineering Research College of Engineering Seoul National University Seoul08826 Korea Republic of Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9783031293146

Virtual reality has become a new option to inform the customers about product before purchasing. However, providing virtual reality may create new challenges. For instance, consumers may obtain essential information about products by using virtual reality option, but eventually they buy their products from the offline channel. This phenomenon is called webrooming. Another challenge is cyber-attacks. E-tailers and their consumers face risks from cyber-attacks. Thus, e-tailers are investing to improve cybersecurity. We consider a dual-channel supply chain consisting of an offline retailer and an e-tailer who purchase the same product from a manufacturer at the same wholesale price. The e-tailer offers a partial refund policy in order to attract the customers. Also, to reduce consumer valuation uncertainty, the e-tailer faces the decision on whether to introduce virtual reality. We analyze three scenarios. Firstly, the e-tailer does not offer virtual reality. Secondly, the e-tailer offers virtual reality, but he does not invest on cybersecurity. Thirdly, the e-tailer offers virtual reality and invests on cybersecurity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Game theory

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Classifying Tweet Sentiment Analysis on Airlines using Random Forest Classifier, Naïve Bayes Classifier and K-Nearest Neighbors Classifier Algorithm

Classifying Tweet Sentiment Analysis on Airlines using Rando...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Alexander, Ivan Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

Opinion has always affected businesses and individuals especially from the Public. People react through social media and spread it incompletely. The situation was then accepted as public opinion. There are three categories of opinion i.e., positive, negative, and neutral. To investigate public opinion on a particular issue of interest, sentiment analysis might be employed. By offering a solution to the text classification challenge, this experiment focuses on the perception of airline tweets among the American public. We offer a method to classify tweets as positive, negative, or neutral using three machine learning classifiers: Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbors Classifier. We examined the performance of three machine learning classifiers in terms of accuracy, average precision, average recall, and average f-1 score. The results demonstrate that the Random Forest Classifier, which produces 82% accuracy, 80% average precision, 82% average recall, and 80% average f1-score, performs the best among the machine learning classifiers. © 2023 IEEE.

关键词： Tweets

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Mental Status Identification for Infertile Patients Using Artificial Intelligence Models 14th

Mental Status Identification for Infertile Patients Using Ar...

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14th International Conference on Frontier Computing, FC 2024

作者： Hsu, Hao-Siang Wang, Jong-Yi Chiou, Hai-Chi Wu, Fu-Hsing Lin, Chuen-Horng Chan, Yung‑Kuan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Health Services Administration China Medical University Taichung406040 Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung404336 Taiwan Department of Management Information Systems National Chung-Hsing University Taichung402202 Taiwan

ISBN: (纸本)9789819623945

Infertile patients may be a high-risk group of mental disorder. The precise identification of the mental status of infertile patients can provide decision support to healthcare professionals and may be helpful in providing preventive measures accordingly. Rare studies focused on the mental status identification using artificial intelligence (AI) models for infertile people. This study aimed to precisely identify the mental status (anxiety and depression) of infertile patients using three AI models, the XGBoost, decision tree, and stacking models. The dataset, retrieved from a structured questionnaire survey in 2011, which included 400 infertility outpatients seeking reproductive treatments at three teaching hospitals in Taiwan was adopted for training and testing AI models. Aforementioned AI models were designed and reached identification performances of accuracy, precision, recall and area under ROC curve (AUC) equaling 0.75–0.82, 0.70–0.80, 0.64–0.72, and 0.64–0.72, respectively. The XGBoost model (accuracy = 0.82, precision = 0.80, recall = 0.72, and AUC = 0.72) outperformed or equaled the decision tree and stacking models in terms of accuracy, precision, recall, and AUC values. Designed AI models for mental status identification can be integrated into the clinical decision support system (CDSS) to provide the anxiety and depression group automatically. Accordingly, the worsening of infertility-related anxiety and depression can be prevented. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： artificial intelligence infertile patients Mental status identification stacking model XGBoost

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IoT In Increasing Human to Machine Interactions in the Automobile Sector 1

IoT In Increasing Human to Machine Interactions in the Autom...

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1st International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Mindarta, Erwin Komara Kavitha, D. Poongundran, Mahizharuvi Priyadarshini, R. Sethu Ram, M. Moldabayeva, Magira Universitas Negeri Automotive Engineering Education Program Malang Indonesia School of Computer Science and Engineering Vellore Institute of Technology India Sri Meenakshi Government Arts College for Women Pg & Research Department of Computer Science Tamil nadu India School of Computer Science and Engineering College Vit Sr.Grade Ii Department Chennai India School of Engineering & Technology Department of Computer Science & Engineering Tirupati India International Education Corporation Department of Linguistics Almaty Kazakhstan

ISBN: (纸本)9781665497107

IoT technologies can facilitate machine-to-machine as well as human-to-machine interactions. Use of an automotive human-machine interface can help in exchanging information between vehicles and passengers or drivers. Organizations in automobile sector are using these technologies for providing their customers with seamless customer-experience, which has helped not only in enhancing profitability of the organization;however also in enhancing customer base. An advanced speech-recognition technology is used in the cars for automation of different functions that ensures extraordinary experience for a driver. This requires adoption of an advanced industrial user-interface that connects the human with the machine actively. Present study has analyzed the ways IoT has played a significant role in making human-to-machine interaction possible in automobile industry. In literature-review, different concepts and theories support the impact of IoT in improving human-machine interactions in automobile industry. This chapter has further analyzed the challenges related to the use of this technology. In methodology section of current study, the ways data has been collected in the study has been analyzed. In findings-section, objectives and variables of the study have been considered for designing relevant themes for providing proper insight into the effectiveness of IoT technologies in improving human -machine interactions in automobile industry. The findings section reveals that IoT usage in this industry can enhance customer-experience by improving human-machine interactions especially when using IoT-enabled cars. It has provided proper suggestions regarding the best ways of utilizing IoT in this sector while ensuring reduction of possible risks. © 2022 IEEE.

关键词： Automotive industry

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The Effect of Bitrate Control Methods of MPEG-DASH Streaming on Multi-View Video and Audio QoE

The Effect of Bitrate Control Methods of MPEG-DASH Streaming...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Toshiro Nunome Yamato Wakayama Department of Engineering Graduate School of Engineering Computer Science Program Nagoya Institute of Technology Nagoya Japan

This paper investigates the effect of bitrate control methods on QoE of multi-view video and audio streaming with MPEG-DASH. We adopt three bitrate control methods for conventional single-view video streaming to the MVV-A system with MPEG-DASH. We conduct a subjective experiment changing available network bandwidth and investigate the effect of the methods on QoE.

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QoE Assessment of Audiovisual Streaming over a Full-Duplex Wireless LAN with Interference Traffic

QoE Assessment of Audiovisual Streaming over a Full-Duplex W...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Toshiro Nunome Daiki Deguchi Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

This paper evaluates the QoE of video and audio transmission over a full-duplex wireless LAN with interference traffic through a computer simulation and a subjective experiment. We employ a simulation environment with a pair of audiovisual transmission and reception terminals and a pair of interference traffic transmission and reception ones. We investigate the effect of the transmission rate of interference traffic and communication distance in a wireless channel on the output quality of the video and audio stream at the reception terminal. We perform a subjective experiment with the output timing of video and audio obtained by the simulation.

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Prospective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers (vol 2, pg 1, 2022)

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INFECTION CONTROL & HOSPITAL EPIDEMIOLOGY 2023年第10期44卷 1705-1705页

作者： Kamineni, Meghana Otles, Erkin Oh, Jeeheh Rao, Krishna Young, Vincent B. Li, Benjamin Y. West, Lauren R. Hooper, David C. Shenoy, Erica S. Guttag, John G. Wiens, Jenna Makar, Maggie Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge Massachusetts Medical Scientist Training Program University of Michigan Medical School Ann Arbor Michigan Department of Industrial and Operations Engineering University of Michigan College of Engineering Ann Arbor Michigan Division of Computer Science and Engineering University of Michigan College of Engineering Ann Arbor Michigan Department of Internal Medicine Division of Infectious Diseases University of Michigan Medical School Ann Arbor Michigan Infection Control Unit Massachusetts General Hospital Boston Massachusetts Division of Infectious Diseases Massachusetts General Hospital Boston Massachusetts Harvard Medical School Boston Massachusetts

Many data-driven patient risk stratification models have not been evaluated prospectively. We performed and compared the prospective and retrospective evaluations of 2 Clostridioides difficile infection (CDI) risk-prediction models at 2 large academic health centers, and we discuss the models’ robustness to data-set shifts.

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